Describing Engagement in the HIV Care Cascade: A Methodological Study
Bibliographic record
Abstract
Introduction: Engagement in the HIV care cascade is required for people living with HIV (PLWH) to achieve an undetectable viral load. However, varying definitions of engagement exist, contributing to heterogeneity in research regarding how many individuals are actively participating and benefitting from care. A standardized definition is needed to enhance comparability and pooling of data from engagement studies. Objectives: The objective of this paper was to describe the various definitions for engagement used in HIV clinical trials. Methods: Articles were retrieved from CASCADE, a database of 298 clinical trials conducted to improve the HIV care cascade (https://hivcarecascade.com/), curated by income level, vulnerable population, who delivered the intervention, the setting in which it was delivered, the intervention type, and the level of pragmatism of the intervention. Studies with engagement listed as an outcome were selected from this database. Results: 13 studies were eligible, of which five did not provide an explicit definition for engagement. The remaining studies used one or more of the following: appointment adherence (n=6), laboratory testing (n=2), adherence to antiretroviral therapy (n=2), time specification (n=5), intervention adherence (n=5), and quality of interaction (n=1). Conclusion: This paper highlights the existing diversity in definitions for engagement in the HIV care cascade and categorize these definitions into appointment adherence, laboratory testing, adherence to antiretroviral therapy, time specification, intervention adherence, and quality of interaction. We recommend consensus on how to describe and measure engagement.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.474 | 0.569 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.017 |
| Bibliometrics | 0.036 | 0.040 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.013 | 0.013 |
| Open science | 0.005 | 0.014 |
| Research integrity | 0.006 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".